This paper presents an image-based method to measure reflectance of a homogeneous flexible object material (usually used in packaging). A point light source and a commercially available RGB camera is used to illuminate and measure the radiance reflected from the object surface in multiple reflection directions. By curving the flexible object onto a cylinder of known radius we are able to record radiance at multiple reflection angles in a faster way. In order to estimate the reflectance and to characterise the material, a spectralon reference tile is used. The spectralon tile is assumed to be homogenous and has near lambertain surface properties. Using Lambert’s cosine law, irradiance at a given point on the object surface is calculated. This information is then used to calculate a BRDF using Phong reflection model to describe the sample surface reflection properties. The measurement setup is described and discussed in this paper along with its use to estimate a BRDF for a given material/substrate. Results obtained indicate that the proposed image-based technique works well to measure light reflected at different planar angles and record information to estimate the BRDF of the sample materials that can be modelled using Phong reflection model. The object material properties, sample curvature and camera resolution decides the number of incident and reflection angles at which the bi-directional reflectance, or the material BRDF, can be estimated using this method.

We describe an analysis method of the omnidirectional color signals in natural scenes. A multiband imaging system with six spectral channels is used for capturing high resolution images in the omnidirectional observations at three locations on campus. The spectral distributions of color signals are recovered using the Wiener estimator from the captured six-band images. The spectral compositions of omnidirectional color signals are investigated based on the PCA of each set of color signals acquired at three locations in different seasons and different times of the day. Three principal components are extracted from three sets of omnidirectional images observed in three different locations. The respective three principal component curves are invariant under seasonal and temporal changes. Moreover, we determine the unified principal components of color signals across all locations. High data compression of omnidirectional images can be achieved. The reliability of the proposed analysis method is confirmed using various experimental data.

We propose a method for automatically classifying multiple objects in a natural scene into metal or dielectric. We utilize
polarization property in order to classify the objects into metal and dielectric, and surface-spectral reflectance in order to
segment the scene image into different object surface regions. An imaging system is developed using a liquid crystal
tunable filter for capturing both polarization and spectral images simultaneously. Our classification algorithm consists of
three stages; (1) highlight detection based on luminance threshold, (2) material classification based on the spatial
distribution of the degree of polarization at the highlight area, and (3) image segmentation based on illuminant-invariant
representation of the spectral reflectance. The feasibility of the proposed method is examined in detail in experiments
using real-world objects.

We describe a method of analyzing the appearance of cosmetic foundation applied to the human face. In particular, we
focus on the "oily-shine" appearance, which is caused by sebum. A multi-band camera system with six spectral channels
is used for the analysis of the oily-shine appearance. As a basic analysis, we examine the optical features of oily-shine
by using two artificial skins looking like make-up skin with oily-shine and without oily-shine. We show that oily-shine
can be defined as the standard dichromatic reflection model. On the basis of the above findings, we propose a method
for detecting oily-shine area. This method involves (1) the extraction candidate areas, and (2) the evaluation of
appearance with oily-shine. First, we capture the CIE XYZ tri-stimulus image of an original make-up face by using the
multi-band camera and after a few hours later, capture the same face as a test facial image. Second, the candidate areas
with oily-shine are extracted by applying the Laplacian operator to luminance Y component of the test facial image.
Third, the principal component analysis is performed on the set of luminance and chromaticity (Y, x, y) of each candidate
area. Light reflection of oil-shine is regarded as the specular component of the dichromatic reflection. Finally, we
determine the existence of oily-shine by comparing specular clusters between the original image and the test image. The
proposed method is tested in experiments with subjective assessment for various real make-up facial images.

This paper proposes a method for analyzing the color characteristics of woodblock prints having oil-based ink and
rendering realistic images based on camera data. The analysis results of woodblock prints show some characteristic
features in comparison with oil paintings: 1) A woodblock print can be divided into several cluster areas, each with
similar surface spectral reflectance; and 2) strong specular reflection from the influence of overlapping paints arises only
in specific cluster areas. By considering these properties, we develop an effective rendering algorithm by modifying our
previous algorithm for oil paintings. A set of surface spectral reflectances of a woodblock print is represented by using
only a small number of average surface spectral reflectances and the registered scaling coefficients, whereas the previous
algorithm for oil paintings required surface spectral reflectances of high dimension at all pixels. In the rendering process,
in order to reproduce the strong specular reflection in specific cluster areas, we use two sets of parameters in the
Torrance-Sparrow model for cluster areas with or without strong specular reflection. An experiment on a woodblock
printing with oil-based ink was performed to demonstrate the feasibility of the proposed method.

The present paper proposes a calibration method of a multispectral camera system using interference filters. A spectral
image processing is effective to acquire an inherent information of an object in a general way. However, filter
registration error often occurs when the interference filter is used. Therefore, a calibration method is presented for
correcting observed images. Moreover, we describe a method for digital archiving of oil paintings based the present
imaging system.

The accuracy of stereo matching depends on precise detection of corresponding points in a pair of stereo images by
template matching. A multiband imaging system captures more than three channels in a visible range. The multiband
imaging technique is useful for improving the accuracy of the stereo matching. This paper proposes an imaging system
and an algorithm for stereo matching based on multiband images. The imaging system is composed of a liquid-crystal
tunable filter and a high sensitive monochrome camera. This imaging system has the advantage that low contrast color
textures that are lost in RGB images could be detected in the multiband-spectral images. We use a modified sequential
similarity detection algorithm (SSDA) for the acceleration of multiband template matching. The similarity is calculated
for each band in the descending order of the variance in template. The template matching is accelerated by quick
discontinuance of the calculation at dissimilar points. Experimental results show that multiband stereo matching is
accurate in comparing with RGB stereo matching. Measurement targets were sheets of color texture patches with small
color differences. The rate of detection of correct corresponding points was 98.4% by multiband stereo matching, while
the rate was 34.7% by RGB stereo matching. Moreover, use of the modified SSDA reduced the CPU time.

A lighting system is proposed to render objects under a variety of colored illumination. The proposed system is
constructed with a LED unit, white diffusion filters, dimmers, and a personal computer as a controller. The LED unit is
composed of four kinds of color LED lamps which are 12 red (R), 14 green (G), 12 blue (B) and 10 white (W) colors.
The LED lamps have a linear input-output relationship and a larger color gamut than Adobe RGB. Since the lighting
system has an independent white light source, white illumination can be produced using the white light source and a
mixture of RGB primary sources. Therefore, to determine illumination color we have to solve a mapping problem from
3D color space to 4D space of RGBW digital values. This paper proposes an effective algorithm for determining the
digital control signals of the RGBW lights, so that colored light is generated with arbitrary (x, y) chromaticity and
luminance value Y. The performance of proposed method is examined in an experiment, where the accuracy of the
colored light is evaluated with regard to the CIE color difference.

We propose a spectral imaging method for material classification and inspection of raw printed circuit boards (PCBs). The method is composed of two steps (1) estimation the PCB surface-spectral reflectances and (2) unsupervised classification of the reflectance data to make the inspection of PCB easy and efficient. First, we develop a spectral imaging system that captures high dynamic range images of a raw PCB with spatially and spectrally high resolutions in the region of visible wavelength. The surface-spectral reflectance is then estimated at every pixel point from multiple spectral images, based on the reflection characteristics of different materials. Second, the surface-spectral reflectance data are classified into several groups, according to the number of PCB materials. We develop an unsupervised classification algorithm incorporating both spectral information and spatial information, based on the Nystrom approximation of the normalized cut method. The initial seeds for the Nystrom procedure are effectively chosen using a guidance module based on the K-means algorithm. Low-dimensional spectral features are efficiently extracted from the original high-dimensional spectral reflectance data. The feasibility of the proposed method is examined in experiments using real PCBs in detail.

This paper describes the investigation and analysis of color terms in modern Japanese. Japanese people use a large
vocabulary of color terms in Japanese unconsciously in their daily life. The authors have studied the basic color terms.
The color vocabulary was investigated for modern Japanese over 6 years, and in all 2,100 subjects participated in the
vocabulary test. This paper shows the investigation process and analyzes the collected color vocabularies from various
points of view. The vocabulary test is based on a questionnaire format without showing any color samples, where each
subject was requested to answer the question items for color names in two levels of importance. Therefore we collected
their recall color names without a priori clue such as color samples. The frequency of occurrence of the responded color
names is statistically evaluated, and then the importance of color names is analyzed from various points of view.

This paper proposes a method for real-time color measurement using active illuminant. A synchronous measurement
system is constructed by combining a high-speed active spectral light source and a high-speed monochrome camera. The
light source is a programmable spectral source which is capable of emitting arbitrary spectrum in high speed. This
system is the essential advantage of capturing spectral images without using filters in high frame rates. The new method
of real-time colorimetry is different from the traditional method based on the colorimeter or the spectrometers. We
project the color-matching functions onto an object surface as spectral illuminants. Then we can obtain the CIE-XYZ
tristimulus values directly from the camera outputs at every point on the surface. We describe the principle of our
colorimetric technique based on projection of the color-matching functions and the procedure for realizing a real-time
measurement system of a moving object. In an experiment, we examine the performance of real-time color measurement
for a static object and a moving object.

An eyegaze interface is one of the key technologies that serves as an input device in a ubiquitous-computing society. Recently, video-based techniques that do not require specific instruments have been studied. With these approaches, development of an accurate iris-extraction algorithm is very important to realize practical eyegaze tracking. For accurate iris extraction, it is necessary to achieve robustness, high speed, and high accuracy. Conventional iris-extraction algorithms experience difficulties in meeting all these requirements simultaneously. This study proposes an iris-extraction algorithm based on the parametric template matching method to satisfy all these requirements at the same time. The parametric template matching method achieves robustness by interpolating among some templates, and the method attains high accuracy by a subpixel matching technique. High-speed matching can be realized by coarse-to-fine matching. To verify the effectiveness of the proposed algorithm, we performed a basic experiment for eyegaze tracking. We show in this experiment that the processing time is approximately 1/900 of that of our previous method and that accuracy is acceptable with the new method. Then, we apply the proposed algorithm to an eyegaze keyboard, along with an imaging system for improving image quality, and we verify the effectiveness of this approach.

A method is proposed for estimating the spectral reflectance function of an object surface by using a six-color scanner.
The scanner is regarded as a six-band spectral imaging system, since it captures six color channels in total from two
separate scans using two difference lamps. First, we describe the basic characteristics of the imaging systems for a HP
color scanner and a multiband camera used for comparison. Second, we describe a computational method for recovering
surface-spectral reflectances from the noisy sensor outputs. A LMMSE estimator is presented as an optimal estimator.
We discuss the reflectance estimation for non-flat surfaces with shading effect. A solution method is presented for the
reliable reflectance estimation. Finally, the performance of the proposed method is examined in detail on experiments
using the Macbeth Color Checker and non-flat objects.

This paper proposes a computer vision system for improving the image quality around a steady gaze point on a display
device. We assume that one observes a localized small area of the displayed image, rather than the whole image, because
of a limited visual angle. The computer vision system consists of two subsystems which are an eyegaze detection
subsystem and an image quality improvement subsystem. The eyegaze detection subsystem tracks a human gaze point on
the display. A tracking algorithm is developed for capturing a human face from a single monocular camera without using
any special devices. The image quality improvement subsystem performs a localized Retinex algorithm. Although the
conventional algorithms contain a large number of complex computations, the localized algorithm is devised for
performing the Retinex computation in high speed for only a localized part within the whole image. The combined
system is developed so that the image quality is improved in real time within just local region around the detected gaze
point. We make an experimental system consisting of an off-the-shelf digital video camera and a personal computer. The
whole performance of the computer vision system is examined experimentally on subjective assessment and processing
time.

A simple and stable method is proposed for distinguishing dielectric and metal material surfaces from the polarization images captured by a vision system consisting of a linear polarizer and a digital camera. The polarization state is determined by the transmitted light intensity through the polarizer as a function of polarizing orientation. The degree of polarization (DOP) is estimated from the image intensities through the polarizer. The DOP map is quite effective for material classification around specular highlight on an object surface. We prove that the DOP map is convex for a dielectric surface and concave for a metal surface. The problem of material classification is then reduced to a simple judgment of the convexity of the DOP map obtained around the highlight peak. The proposed method is not a pixelwise local method based on thresholding the Fresnel ratio computed at each pixel but an area-based method based on the DOP map in a highlight area. The feasibility of the method is confirmed in experiments under a variety of conditions.

We propose a technique for viewpoint and illumination-independent digital archiving of art paintings in which the painting surface is regarded as a 2-D rough surface with gloss and shading. Surface materials like oil paints are inhomogeneously dielectric with the dichromatic reflection property. The procedure for total digital archiving is divided into three main steps: acquisition, analysis, and rendering. In the first stage, we acquire images of a painting using a multiband imaging system with six spectral channels at different illumination directions. In the second stage, we estimate the surface properties of surface-spectral reflectance functions, surface normal vectors, and 3-D reflection model parameters. The principal component analysis suggests that the estimated spectral reflectances have the potential for high data compression. In the third stage, we combine all the estimates for rendering the painting under arbitrary illumination and viewing conditions. We confirm the feasibility of the proposed technique in experiments using oil paintings.

This paper describes a method for measurement and analysis of surface reflection properties of oil paints under
a variety of conditions. First, the radiance factor of a painting surface is measured at different incidence and viewing
angles by using a gonio-spectro photometer. The samples are made from different oil paint materials on supporting
boards with different paint thicknesses. Next, typical reflection models are examined for describing 3D reflection of the
oil painting surfaces. The models are fitted to the observed radiance factors from the oil paint samples. The Cook-
Torrance model describes well the reflection properties. The model parameters are estimated from the least-squared
fitting to the genio-photometric measurements. Third, the reflection properties are analyzed on the basis of several
material conditions such as pigment, supporting material, oil quantity, paint thickness, and support color.

A method is developed for estimating an omnidirectional distribution of the scene illuminant spectral distribution, including spiky fluorescent spectra. First, we show a measuring apparatus, consisting of the mirrored ball system and the imaging system using a LCT filter (or color filters), a monochrome CCD camera, and a personal computer. Second, the measuring system is calibrated and images representing the omnidirectional light distribution are created. Third, we present an algorithm for recovering the illuminant spectral-power distribution from the image data. Finally,
the feasibility of the proposed method is demonstrated in an experiment on a classroom scene with different illuminant sources such as fluorescent light, incandescent light, and daylight. The accuracy of the estimated scene illuminants is shown in the cases of the 6-channel multi-band camera, 31-channel spectral camera, and 61-channel spectral camera.

A method is proposed for estimating the spectral reflectance of made-up skin color under various conditions including the undesirable colored skin. The color of dark spot is caused by increasing the component of melanin. The reddish skin is caused by the increase of hemoglobin. Our method uses the Kubelka-Munk theory to calculate the surface spectral reflectance human skin. This theory calculates the reflectance and transmittance of the light passing through a turbid medium from the absorption and scattering of the medium. The spectral reflectance of made-up skin is estimated by adjusting parameters of the thickness of the makeup layer. The proposed estimation method is evaluated on an experiment in detail. First, we measure the spectral reflectance of facial skin under the three conditions of normal skin, undesirable skin, and made-up skin. The undesirable skin includes stain, suntan or ruddy skin. The made-up skin means the skin with foundation on the normal skin, the stain, the suntan and the ruddy skin. Second, we estimate the spectral reflectance of made-up skins from the reflectance of bare skins and optical characteristics of foundations. Good coincidence between the estimated reflectance and the direct measurement shows the feasibility of the proposed method.

The present paper proposes a nonlinear approach using a neural network for color control of projection displays, including the LCD and DLP types. This approach accepts variations in primary color coordinates and coupling among RGBW channels. We regard a display system as an unknown nonlinear system with RGB signal inputs and XYZ tristimulus outputs. We determine the RGB values so that the projector outputs the desired XYZ values. The neural network is used for estimating adaptively an inverse mapping from the XYZ space to the RGB space. Because of a direct mapping, we can eliminate the need to predict white channel separation. Moreover, we present a method for correcting the emitted luminance, according to the spatial location and the surface color of a screen. The spatial correction is needed because a color image from a computer is not displayed uniformly on the screen. Also the screen color correction is needed because color reproduction is based on the light reflected from the screen. The correction makes it possible to reproduce accurate color images on a colored wall at any location.

A method is proposed for estimating fluorescent scene illumination with spiky spectrum by using a spectral imaging system. The system is composed of a LCT filter, a monochrome CCD camera, and a personal computer. The gray world assumption is used for estimating the illuminant spectrum from the camera outputs. It is possible to infer the material type of fluorescent light source by knowing the wavelengths of spikes on the estimated illuminant spectrum. A procedure is proposed for classifying scene illuminants into two steps; the gross classification of classifying the illuminant into three groups and the detailed classification of determining a unique fluorescent type. Algorithms are developed for peak detection using the second derivative spectrum for the gross classification and sensor correlation using the background continuum for the detailed classification. Finally, the feasibility of the proposed method is demonstrated in an experiment using six real fluorescent lamps.

A method is proposed for estimating various parameters of a reflection model using both the image data and the range data of an object surface. A unified measuring system, combining a laser range and a multi-band camera system, is made for acquiring the 3D shape data and the spectral reflectance data of the object surface. First, the diffuse reflection component and the specular reflection component at every pixel are obtained using the observed images at
two illumination directions and the surface normal vectors calculated from the range data. The spectral reflectance is then estimated from the diffuse reflection component. Next, the extracted specular reflection component is fitted to the specular function of the Torrance-Sparrow model. The performance of the proposed method is examined on an experiment using a painted object in details. We show the estimation results for (1) spectral reflectance, (2) surface
roughness, and (3) diffuse and specular intensities. The overall feasibility of the proposed method is confirmed based on computer graphics images created by using the estimated parameters.

The present paper describes a method for modeling human skin coloring and estimating the surface-spectral reflectance by using the Kubelka-Munk theory. First, human skin is modeled as two layers of turbid materials. Second, we describe the reflectance estimation problem as the Kubelka-Munk equations with unknown six parameters. These parameters are the regular reflectance at skin surface and the five weights for spectral absorption of such different pigments as melanin, carotene, oxy-hemoglobin, deoxy-hemoglobin, and bilirubin. Moreover, the optical coefficients of spectral absorption and scattering for the two skin layers and the thickness values of these layers are used for the solution. Finally, experiments are done for estimating the skin surface-spectral reflectance on some body parts, such as the cheeks of human face, the palm, the backs of hand, the inside of arm, and the outside of arm. It is confirmed that the proposed method is more reliable in all cases.

This paper describes practical algorithms and experimental results using the sensor correlation method. We improve the algorithms to increase the accuracy and applicability to a variety of scenes. First, we use the reciprocal scale of color temperature, called 'mired,' in order to obtain perceptually uniform illuminant classification. Second, we propose to calculate correlation values between the image color gamut and the reference illuminant gamut, rather than between the image pixels and the illuminant gamuts. Third, we introduce a new image scaling operation with an adjustable parameter to adjust overall intensity differences between images and find a good fit to the illuminant gamuts. Finally, the image processing algorithms incorporating these changes are evaluated using a real image database.

This paper describes a method for estimating a reflection model from a color image of an object taken by a multi-band CCD camera. The Torrance-Sparrow model is used for modeling light reflection on an object surface. We propose algorithms for estimating model parameters from a single image by the multi-band CCD camera. To estimate the surface roughness, we propose the use of the brightness image and the reflectance map in the neighborhood of a highlight peak point. An algorithm is presented for finding a particular solution of the surface orientation. The feasibility of the method is demonstrated in an experiment using a painted object. The estimation accuracy of the whole model is confirmed based on computer graphics images.

The present paper proposes a method for recoding and rendering of art paintings using only spectral reflectance data of the object surfaces. A multiband camera system with six spectral channels of fixed wavelength bands is used for spectral imaging. No range finder is used for measuring the surface shape. We show that it is possible to render realistic images of the object for different directions of illumination, without using the 3D shape data. First, a method for estimating spectral reflectance for body reflection component of a rough surface is described. Next a method is proposed for practical image rendering. The method is based on interpolation among the images reproduced in the known illumination directions. The color signal for an arbitrary illumination direction is estimated from the color signals observed for three illumination directions. As a result, the image of an art painting object illuminated from any direction is rendered using the reflectance data obtained for three illumination directions. We present algorithms for estimating surface-spectral reflectances of an object and rendering the image for any lighting conditions. An experiment using an oil painting is executed for demonstrating the feasability of the proposed method.

We introduce an image database called Natural Image Database of collecting natural color images that can be used via the Internet, and then show how it is used for illuminant classification and evaluate our sensor correlation method. A significant feature of the present database is inclusion of both image and illuminant data. It is not only a collection of color images taken a CCD camera, but also includes the calibration data of the camera used for image acquisition and the illuminant spectral data measured by a spectro-radiometer for each scene. Therefore, illuminant estimation algorithms can be evaluated on various natural scenes and comparison with the direct measurements. The illuminant classification is based on correlation computation between the gamut of an observed image and each of the reference illuminant gamuts. The illuminant gamuts of blackbody radiators are created from 2500K to 8500K in the RB sensor plane. The image gamut is defined as the convex hull of pixel values of the observed image. Then, one illuminant gamut is selected as the estimated illuminant, which gives an overall maximum of the correlation function. An experiment using the image database is executed for demonstrating the feasibility.

Anew color image segmentation algorithm is presented in this paper. This algorithm is invariant to highlights and shading. This is accomplished in two steps. First, the average pixel intensity is removed form each RGB coordinate. This transformation mitigates the effects of highlights. Next, the Mixture of Principal Components algorithm is used to perform the segmentation. The MPC is implicitly invariant to shading due to the inner vector product or vector angle being used as similarity measure. Since the new coordinate system contains negative numbers, it is necessary to modify the MPC algorithm since in its original form it does not distinguish between positive and negative color space coordinates. Results on artificial and real images illustrate the effectiveness of the method. Finally, the use of the total within-cluster variance is investigated as possible criterion for selecting the number of clusters for the new algorithm.

A lighting system is proposed for acquiring color images under a variety of illuminations. This system is constructed with halogen lamps, color filters, white diffusion filters, dimmers, and a personal computer as a controller. Colored light with continuous spectral power distribution is generated based on the additive color mixture of RGB primary lights. First, we describe a method for generating light of a desired color stimulus value. The basic procedure is performed in two steps: (1) XYZ-RGB color coordinate conversion and (2) correction of nonlinearities. A practical procedure is presented for generating colored light with (x,y) chromaticity coordinates of any value within a specified color gamut.

A set of multi-channel camera systems and algorithms is described for recovering both the surface spectral- reflectance function and the illuminant spectral-power distribution from the data of spectral imaging. We show the camera system with six spectral channels of fixed wavelength bands. This system is created by using a monochrome CCD camera, six different color filters, and a personal computer. The dynamic range of the camera is extended for sensing the high intensity level of highlights. We suppose in a scene that the object surface of an inhomogeneous dielectric material is described by the dichromatic reflection model. The process for estimating the spectral information is composed of several steps of (1) the finite- dimensional linear model representation of wavelength functions, (2) illuminant estimation, (3) data normalization and image segmentation, (4) reflectance estimation. The reliability of the camera system and the algorithms is demonstrated in an experiment. Finally a new type of system using liquid crystal filters is briefly introduced.

A method is described for realizing an exact color reproduction on a printer using more than three color inks. The CIE-L*a*b* color system is used as the device-independent color space. The mapping from the L*a*b* color space to the printer color space is constructed using a neural network. This mapping does not use such techniques as UCR and GCR. The problem in four-color printing is considered as the problem of controlling an unknown system with four inputs and three outputs. We present a two-phase procedure for solving this control problem. The first phase determines a printer model, and the second phase determines the combined network system of a printer model and a controller so as to provide the identity mapping. This technique is applied to the color control of a six-color printer using CMYK plus light Cyan and light Magenta.

Neural network methods are described for color coordinate conversion between color systems. We present solutions for two problems of (1) conversion between two color-specification systems and (2) conversion between a color-specification system and a device coordinate system. First we discuss the color-notation conversion between the Munsell and CIE color systems. The conversion algorithms are developed for both directions of Munsell-to-L*a*b* and L*a*b*-to-Munsell. Second we discuss a neural network method for color reproduction on a printer. The color reproduction problem on the printer using more than four inks is considered as the problem of controlling an unknown system. The practical algorithms are presented for realizing the mapping from the L*a*b* space to the CMYK space. Moreover the method is applied to the color control using CMYK plus light cyan and light magenta.

A method is proposed for solving the mapping problem from the 3D color space to the 4D CMYK space of printer ink signals by means of neural network. The CIE-L*a*b* color system is used as the color space. The color reproduction problem is considered as the problem of controlling an unknown static system with four inputs and three outputs. A controller finds the CMYK signals necessary to produce the desired L*a*b* values from a printer. Our solution method for this control is based on a two-phase procedure. Validity of our method is shown in an experiment using a dye sublimation printer.

The present paper describes a color classification method that partitions color image data into
a set of uniform color regions. The ability to classify spatial regions of the measured image into a
small number of uniform regions can be useful for several problems including image segmentation
and image representation. First, the input image data are mapped from device coordinates into an
approximately uniform perceptual color space. Colors are classified by means of cluster detection
in the uniform color space. The process is composed of two stages of basic classification and
reclassification. The basic classification is based on histogram analysis to detect color clusters
sequentially. The principal components of the color data are extracted for effective discrimination of
clusters. At the reclassification stage, the extracted representative colors by the basic classification
are reclassified on a color distance. The performance of the method is discussed in an experiment
using a picture of paper objects.

This paper describes a method for estimating the surface spectral reflectance function of inhomogeneous
objects. The standard reflectance model for inhomogeneous materials suggests that surface
reflectance functions can be described as the sum of a constant (specular) function and a subsurface
( diffuse) function. First we present an algorithm to generate an illuminant estimate without using a
reference white standard. Next we show that several physical constraints on the reflectance functions can
be used to estimate the subsurface component. A band of the estimated spectral reflectance functions
is recovered as possible solutions for the subsurface component.

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